MOTIVATION: Network inference algorithms are powerful computational tools for identifying putative causal interactions among variables from observational data. Bayesian network inference algorithms hold particular promise in that they can capture linear, non-linear, combinatorial, stochastic and other types of relationships among variables across multiple levels of biological organization. However, challenges remain when applying these algorithms to limited quantities of experimental data collected from biological systems. Here, we use a simulation approach to make advances in our dynamic Bayesian network (DBN) inference algorithm, especially in the context of limited quantities of biological data. RESULTS: We test a range of scoring metrics and search heuristics to find an effective algorithm configuration for evaluating our methodological advances. We also identify sampling intervals and levels of data discretization that allow the best recovery of the simulated networks. We develop a novel influence score for DBNs that attempts to estimate both the sign (activation or repression) and relative magnitude of interactions among variables. When faced with limited quantities of observational data, combining our influence score with moderate data interpolation reduces a significant portion of false positive interactions in the recovered networks. Together, our advances allow DBN inference algorithms to be more effective in recovering biological networks from experimentally collected data. AVAILABILITY: Source code and simulated data are available upon request. SUPPLEMENTARY INFORMATION: http://www.jarvislab.net/Bioinformatics/BNAdvances/

Forecasting is an instrumental tool for strategic decision-making in any business activity. Good forecasts can reduce the uncertainty about the future and, hence, help managers make better decisions. Virtually all statistical forecasting techniques depend on the continuity of historical data and time series and may not predict a discontinuous change in the business environment. Often times, this discontinuity is known to managers who then must rely on their judgment to make forecast adjustments. We discuss the role of judgmental forecasting and take the problem of estimating future hotel room demand as a practical business application. Next, we propose IS-JFK: an intelligent system to support judgmental forecasting and knowledge of managers. To account for vagueness in the knowledge elicited from managers and the approximate nature of their reasoning, the system is built around fuzzy IF-THEN rules and uses fuzzy logic for decision inference. IS-JFK supports two methods for forecast adjustments: 1) a direct approach and 2) an approach based on fuzzy intervention analysis. Actual data from a hotel property are used in some case-scenario simulations to illustrate the merits of the intelligent support system.

Diagnostic problems are proposed to be solved via the pattern recognition approach. The main example has been motivated by economic macro modelling, hence a reference model approach is adopted. The dynamic systems under consideration are assumed to be linear in three case studies and nonlinear in one case study. All four case studies illustrate the basic concept of this approach. Since the state vector represents the most compact information about the dynamic system, a measured/estimated state vector is chosen as the feature vector of a specific pattern vector. The results obtained verify our belief that this approach can be very useful if the assumption of linearity has been met. It is worthwhile to point out that although this approach appears fairly straightforward, it is quite powerful since computational power is now readily available. Since information systems with large databases are readily available, we believe the approach presented in the paper are certainly practical. The first case study deals with a macro economic model, while the second deals with a three state variable linear difference equation model. The third example illustrates use of patterns in frequency domain to make inference of a dynamic system diagnostic in time domain. Finally, the investigation focuses on the simulation of a nonlinear system. It is quite certain to conclude that the nonlinear system behavior is very dependent and sensitive to parameter changes.

In this paper, we present a complete design framework for an adaptive multiple agent fuzzy constraint-based controller (MAFCC) based on fuzzy penumbra constraint processing in each fuzzy constraint subnetwork collaborating with a connected constraint network and its corresponding semantic modeling in a first-order predicate calculus (FOPC) language, with application to a complex hydraulic system. The concept of “multiple agent” and “fuzzy constraint subnetwork” in a complex control system is introduced and some basic definitions of penumbra fuzzy constraint processing in a constraint subnetwork and the collaboration with an overall connected constraint network and its semantic modeling are addressed. As a result, a human agent interacts with system agents and allows the constraints to be added or deleted on-line according to the constraints imposed from the outside environment. Near-optimal system performance is accomplished by restricting all the penumbra constraints to be satisfied in each constraint subnetwork simultaneously which are interconnected as a result of constraints that exist between each of them. Following the principle of constraint satisfaction and fuzzy local propagation reasoning, each individual system agent is now constrained to behave in a certain fashion as dictated by the overall constraint network. In addition, the constraint network in MAFCC system provides an update strategy which makes a real time adaptive hydraulic control for all 20 cities possible.

A complete design framework for a fuzzy constraint-based controller based on fuzzy-constraint processing and its semantics and relationship to fuzzy logic is presented. In this paper, the concept of "fuzzy constraints" in problem solving is introduced, and some basic definitions of fuzzy-constraint processing in a constraint network and its semantic modeling are addressed. Then a fuzzy local propagation inference mechanism for reasoning about imprecise information applying the filter operation in a network of constraints is proposed. Moreover, we advance the concurrent fuzzy-logic controller (FLC) to a new type of controller, the fuzzy constraint-based controller (FCC), using a more general predicate calculus and full first-order logic knowledge representation and making use of the idea of fuzzy-constraint processing to model practical dynamic control systems. Finally, simulation results show that a FCC achieves equivalent performance as PD type and PI type FLCs and it also demonstrates superior outcomes to a conventional PID controller in terms of rise time and peak-percent overshoot.

Interim results of a study on pattern recognition of robotic tactile impressions using connectionist models are described. The training data consists of gray-scale force gradient profiles that accurately reflect the tactile domain; the focus is on extracting these features with artificial neural systems (ANSs). A description is given of an architecture in which a two-layer back-error-propagation network performs feature extraction of gray-scale gradients, and a second BEP network classifies the surface profiles. Imposition of constraints on the training set is critical to ensure that meaningful features are selected. In domains where information content of the input vectors are dense and very similar, receptive field neurons encode useful data across unit activations, while fully connected schemes shroud information among the link weights

Design criteria- α, β and γ parameters- for an arrayed touch-sensor are proposed. Pattern recognition schemes for static images are proposed and their performances are evaluated in terms of proposed criteria. Possible causes of deteriorating the image information are considered; their effects to the pattern recognition are also investigated.

The application of pattern recognition to quality control is shown. The well-known methodology of conventional quality control is described briefly to illustrate the philosophy of quality measurement. The advantages of adopting the pattern classification approach in quality control (or quality assurance) include on-line quality monitoring without much alteration of existing conventional systems, instant diagnosis of the production process, and ultimately, automatic feedback control of the overall manufacturing process through the development of a corrective control policy.

An adaptive sequential bit synchronizer for a random squarewave data process is proposed in this paper. The probability distributions of the signal parameters and noise process are assumed unknown. The synchronization algorithms, implemented on a small computer, are capable of accurately estimating pulse transition time, pulse amplitude, and pulsewidth simultaneously by invoking the Gauss-Markov theorem. Numerous simulations indicate that the performance of the synchronizer is satisfactory from the points of view of the amount of data required and the speed of convergence.